A dynamic rate-based optical storage charging station energy efficiency management method and device

Abstract

The embodiment of the specification discloses a kind of based on dynamic rate's light storage charging station energy efficiency management method and device, the method includes obtaining historical photovoltaic power, historical charging load and historical weather data, based on the historical photovoltaic power, the historical charging load and the historical weather data are calculated to obtain photovoltaic predicted power and charging load predicted value, based on the photovoltaic predicted power and the charging load predicted value, power balance constraint condition is established;Obtain light storage charging data, the light storage charging data includes grid data and energy storage data, based on the grid data and the energy storage data, establish objective function, based on the objective function, the constraint condition established the energy storage charge-discharge plan sequence and grid interaction plan sequence are obtained.Thereby avoid when from grid purchase electricity at peak electricity price, by low suction high discharge to reduce the entire light storage charging (photovoltaic-energy storage-charging) integrated power station operating cost.

Description

Technical Field

[0001] This invention relates to the fields of microgrid energy management and electric vehicle charging technology, and in particular to a method, device, electronic equipment and storage medium for energy efficiency management of photovoltaic-storage charging stations based on dynamic rates. Background Technology

[0002] With the popularization of new energy vehicles, integrated photovoltaic-storage-charging (PV-SSD-Charging) power stations have become an important infrastructure for solving the problem of charging load impacting the power grid and absorbing new energy. However, the current energy management strategies of PV-SSD-charging stations have the following drawbacks: Traditional PV-SSD-charging stations often adopt a "peak shaving and valley filling" strategy, that is, charging the energy storage battery during off-peak hours (such as at night) and discharging it for electric vehicle use or selling electricity to the grid during peak hours (such as during the day) to save electricity costs or generate revenue. This strategy is usually based on fixed time periods (e.g., 23:00–7:00 per day is considered off-peak electricity), but in reality, electricity prices in the electricity market (especially the spot market) fluctuate in real time, possibly changing every 15 minutes or even every 5 minutes, resulting in high operating costs for the entire integrated PV-SSD-charging power station. Summary of the Invention

[0003] To address the problems existing in the prior art, this specification describes a method, apparatus, electronic device, and storage medium for energy efficiency management of photovoltaic-storage charging stations based on dynamic rates through one or more embodiments.

[0004] According to the first aspect, a method for energy efficiency management of photovoltaic-storage-charging stations based on dynamic pricing is provided, the method comprising:

[0005] Historical photovoltaic power, historical charging load, and historical weather data are acquired. Based on the historical photovoltaic power, historical charging load, and historical weather data, the predicted photovoltaic power and predicted charging load values ​​are calculated. Power balance constraints are established based on the predicted photovoltaic power and predicted charging load values.

[0006] Acquire photovoltaic-storage-charging data, which includes grid data and energy storage data. Establish an objective function based on the grid data and energy storage data. Obtain an energy storage charging and discharging plan sequence and a grid interaction plan sequence based on the objective function and the established constraints. Perform battery charging and discharging based on the energy storage charging and discharging plan sequence and perform grid interaction based on the grid interaction plan sequence.

[0007] Preferably, the energy storage data includes the minimum allowable capacity of the battery, the maximum allowable capacity of the battery, and the current battery capacity. The method further includes:

[0008] A first energy storage constraint is established based on the minimum allowable capacity of the battery, the maximum allowable capacity of the battery, and the current capacity of the battery.

[0009] Preferably, the energy storage data also includes the energy storage battery charge / discharge efficiency and the battery's rated capacity, and the method further includes:

[0010] A second energy storage constraint is established based on the energy storage battery's charge and discharge efficiency and the battery's rated capacity. The second energy storage constraint is used to calculate the battery's state of charge at the next moment.

[0011] The state of charge (SOC) of the battery is collected at set time intervals. If the SOC collected by the battery is not equal to the calculated SOC, the energy storage charging and discharging plan sequence and the grid interaction plan sequence are recalculated.

[0012] Preferably, the method further includes:

[0013] When the state of charge of the collected battery is not equal to the calculated state of charge of the battery, the deviation value between the collected state of charge of the battery and the calculated state of charge of the battery is calculated. If the deviation value exceeds the set deviation threshold for multiple consecutive cycles, the calculation of the energy storage charging and discharging plan sequence and the grid interaction plan sequence is stopped and an alarm message is sent.

[0014] Preferably, the method further includes:

[0015] If the grid voltage is not within the set grid voltage range, the calculation of the energy storage charging and discharging plan sequence and the grid interaction plan sequence is stopped and an alarm message is sent.

[0016] Preferably, the energy storage data further includes power display of the PCS converter and capacity display of the transformer or grid connection point, and the method further includes:

[0017] A first device power constraint condition is established based on the maximum power display of the PCS converter.

[0018] A second equipment power constraint condition is established based on the maximum capacity of the transformer or grid connection point.

[0019] Preferably, the method further includes:

[0020] After acquiring the photovoltaic-storage-charging data, the data is processed for missing values, outliers, and normalization.

[0021] According to the second aspect, a photovoltaic-storage-charging station energy efficiency management device based on dynamic pricing is provided, the device comprising:

[0022] The photovoltaic-storage-charging data preprocessing module is used to acquire historical photovoltaic power, historical charging load and historical weather data, calculate the photovoltaic predicted power and charging load predicted values ​​based on the historical photovoltaic power, historical charging load and historical weather data, and establish power balance constraints based on the photovoltaic predicted power and charging load predicted values.

[0023] The photovoltaic-storage-charging energy efficiency management module is used to acquire photovoltaic-storage-charging data, which includes grid data and energy storage data. An objective function is established based on the grid data and the energy storage data. Based on the objective function and the established constraints, an energy storage charging and discharging plan sequence and a grid interaction plan sequence are obtained. Battery charging and discharging are performed based on the energy storage charging and discharging plan sequence, and grid interaction is performed based on the grid interaction plan sequence.

[0024] According to a third aspect, an electronic device is provided, including a processor and a memory;

[0025] The processor is connected to the memory;

[0026] The memory is used to store executable program code;

[0027] The processor runs a program corresponding to the executable program code stored in the memory to perform the steps of the method provided as in the first aspect or any possible implementation thereof.

[0028] According to a fourth aspect, a computer-readable storage medium is provided having a computer program stored thereon, the computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the method provided as in the first aspect or any possible implementation thereof.

[0029] The beneficial effects of this invention are as follows:

[0030] 1. The method and apparatus provided in the embodiments of this specification predict the photovoltaic power and charging load forecast values ​​for future times by using historical photovoltaic power, historical charging load and historical weather data. Then, the optimal energy storage charging and discharging plan sequence and grid interaction plan sequence are calculated according to the objective function, thereby avoiding purchasing electricity from the grid during peak electricity prices and reducing the operating cost of the entire photovoltaic-energy storage-charging integrated power station by low absorption and high release. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating a dynamic rate-based energy efficiency management method for photovoltaic-storage charging stations, as implemented in this manual.

[0033] Figure 2 This is a schematic diagram of the structure of a photovoltaic energy storage charging station energy efficiency management device based on dynamic rates, as specifically implemented in this specification.

[0034] Figure 3 This is a schematic diagram of the structure of an electronic device used in a specific implementation of this specification. Detailed Implementation

[0035] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0036] In the following description, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The following description provides multiple embodiments of this application, which can be substituted or combined with each other. Therefore, this application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then this application should also be considered to include embodiments containing one or more other possible combinations of A, B, C, and D, even if such embodiments are not explicitly described in the following text.

[0037] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this application. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0038] See Figure 1 , Figure 1 This is a flowchart illustrating the energy efficiency management method for photovoltaic-storage-charging stations based on dynamic pricing provided in this application embodiment. In this application embodiment, the method includes:

[0039] S101. Obtain historical photovoltaic power, historical charging load and historical weather data; calculate the predicted photovoltaic power and predicted charging load based on the historical photovoltaic power, historical charging load and historical weather data; establish power balance constraints based on the predicted photovoltaic power and predicted charging load.

[0040] S102. Acquire photovoltaic-storage charging data, which includes grid data and energy storage data. Establish an objective function based on the grid data and the energy storage data. Obtain an energy storage charging and discharging plan sequence and a grid interaction plan sequence based on the objective function and the established constraints. Perform battery charging and discharging based on the energy storage charging and discharging plan sequence and perform grid interaction based on the grid interaction plan sequence.

[0041] The implementing entity of this application may be the control unit of an integrated photovoltaic-storage-charging power station.

[0042] In the embodiments described in this specification, historical photovoltaic power, historical charging load, and historical weather data are acquired. This data is collected by sensors over the past 7 days, with a set time interval. Given the predicted time period T, the LSTM or XGBoost algorithm is used to calculate the next T time periods (e.g., within the next 24 hours, T=24, with each point occurring every 15 minutes). The projected photovoltaic (PV) power and charging load are used to determine the power balance constraints. PV-storage-charging data is acquired, including grid data and energy storage data. Grid data refers to the dynamic time-of-use (TOU) price curves for the next 24 hours obtained from power trading platforms or DSOs (Distribution System Operators). This represents the electricity price at time t. Energy storage data includes battery SOC, SOH (state of health), charge / discharge rate limits, and battery temperature. An objective function is established based on the grid data and energy storage data. Based on the objective function and the established constraints, an energy storage charge / discharge plan sequence and a grid interaction plan sequence are obtained. The energy storage charge / discharge plan sequence is defined for each time interval. The sequence of actions performed on the battery, such as charging at 8:00, discharging at 8:15, etc., is the grid interaction plan sequence for each time interval. The application describes a sequence of interactions between the grid and the power grid, such as buying electricity at 8:00 AM and selling electricity at 8:15 AM. This application uses historical photovoltaic power, historical charging load, and historical weather data to predict future photovoltaic power and charging load. Then, based on an objective function, it calculates the optimal energy storage charging and discharging plan sequence and the grid interaction plan sequence. This avoids purchasing electricity from the grid during peak electricity prices and reduces the overall operating cost of the integrated photovoltaic-energy storage-charging power station by absorbing electricity at low prices and discharging it at high prices.

[0043] Specifically, the objective function is:

[0044] in, Let t be the electricity cost of interacting with the power grid at time t. Let be the lifespan degradation cost of the energy storage battery at time t.

[0045] Specifically:

[0046]

[0047] in, Indicates the power exchange between the power grids. Indicates purchasing electricity. Indicates selling electricity. The dynamic electricity price at time t can be obtained from the electricity trading platform or the DSO (Distribution System Operator).

[0048] Specifically:

[0049] in, Indicates the charging and discharging power of the energy storage battery. Indicates discharge. Indicates charging. It is the battery depreciation factor for single throughput capacity, used to prevent frequent and unprofitable charging and discharging.

[0050] In one possible implementation, after acquiring the photovoltaic energy storage and charging data, the collected data is preprocessed. The preprocessing operations include missing value handling, outlier handling, and normalization. Missing value handling involves filling in missing data caused by communication packet loss using linear interpolation if the missing duration is less than 30 minutes, and using the historical average of similar days (e.g., weekdays / holidays) if the missing duration is greater than 30 minutes. Outlier handling involves removing data exceeding physical limits (e.g., SOC > 100% or illuminance < 0). Normalization includes performing normalization according to the following formula.

[0051] in, The value after normalization. This represents the maximum value of this data type. This is the minimum value of this type of data. These are the values ​​before normalization. By preprocessing the collected photovoltaic-storage-charging data to discard invalid values ​​and supplement differences, the accuracy of the subsequently calculated energy storage charging and discharging plan sequence and the grid interaction plan sequence is ensured.

[0052] In one possible implementation, the energy storage data includes the minimum allowable capacity of the battery, the maximum allowable capacity of the battery, the charge / discharge efficiency, the rated capacity of the battery, and the current power of the inverter. The energy efficiency management method for photovoltaic-storage charging stations based on dynamic rates further includes: setting a first energy storage constraint and a second energy storage constraint, wherein the first energy storage constraint is:

[0053] in, This represents the state of charge (i.e., the current percentage of battery charge) at time t. Indicates the maximum allowable battery capacity. This indicates the minimum allowable charge level of the battery.

[0054] The second energy storage constraint is:

[0055]

[0056] in, This represents the state of charge of the battery at time t. express The state of charge of the battery at all times. Indicates the charging and discharging power of the energy storage battery. For charging and discharging efficiency, This refers to the battery's rated capacity.

[0057] In one possible implementation, the energy storage data also includes power display of the PCS converter and capacity display of the transformer or grid connection point, based on the power constraints of the constructed equipment:

[0058]

[0059] in, This indicates the maximum power display (hardware limit) of the PCS converter. This indicates the maximum capacity limit of the transformer or grid connection point, limiting the displayed capacity of the transformer or grid connection point to a certain range to prevent tripping.

[0060] In one possible implementation, based on the time interval set above... At time t, based on the energy storage charge / discharge plan sequence and the grid interaction plan sequence, only the charge / discharge actions and grid interaction actions corresponding to time t are executed. Then, based on the second energy storage constraint condition mentioned above, the battery's state of charge (SOC) at time t+1 is calculated. At time t+1, the battery's SOC is collected. If the collected SOC is compared with the calculated SOC at time t+1, and they are not equal, the energy storage charge / discharge plan sequence and the grid interaction plan sequence are recalculated. By comparing the collected battery SOC with the calculated SOC at time t+1, and recalculating the energy storage charge / discharge plan sequence and the grid interaction plan sequence when they are not equal, it is ensured that the actions at time t+2 are calculated based on the actual values ​​at time t+1, thereby avoiding error accumulation and ensuring the accuracy of the entire system's operation.

[0061] As an example, suppose it is currently 10:00 AM, and the battery's current SOC is 50%. The first round of calculations at 10:00 AM involves the energy storage charge / discharge plan sequence and its interaction with the grid. The sequence is as follows: 10:00 - 10:15: Electricity prices are cheap, charging is recommended, and the calculated SOC will reach 55% at 10:15. 10:15 - 10:30: Someone wants to charge, discharging is recommended... The system only issues a "charge" command to the device, charging for 15 minutes. During the period from 10:00 to 10:15, some unexpected issues arise: the photovoltaic power is not as strong as predicted. As a result, at 10:15, the sensor measures the battery's actual SOC at only 52% (instead of the 55% predicted in the previous round). At 10:15, the SOC = 52% is used as the new starting point to calculate the energy storage charging and discharging plan sequence and the grid interaction plan sequence. The new sequence is: 10:15 - 10:30 is changed to "low power discharge" or "purchase electricity from the grid to supplement".

[0062] In one possible implementation, if the actual output of the battery (i.e., the state of charge of the battery collected at time t+1) deviates from the command (i.e., the state of charge of the battery at time t+1 calculated according to the second energy storage constraint) by more than 10% within three consecutive time intervals, or if the grid voltage is abnormal, i.e., the collected grid voltage is not within the set grid voltage range, then the calculation of the energy storage charging and discharging plan sequence and the grid interaction plan sequence will be stopped. Priority will be given to ensuring that the charging pile can supply power to the new energy vehicle, ensuring the stable operation of the entire photovoltaic-energy storage-charging integrated power station, and sending alarm information to the cloud.

[0063] As an example, in a certain region, peak electricity prices are from 2:00 PM to 4:00 PM in summer, and off-peak prices are from 12:00 AM to 6:00 AM. Solar power reaches its peak at noon. System actions: Early morning: The system predicts a large charging demand during the day and controls energy storage to fully charge during off-peak hours (12:00 AM to 6:00 AM). Noon: At noon, solar power output is high, prioritizing charging piles, with excess power replenishing the batteries (if not fully charged). Peak: At 2:00 PM, electricity prices surge to peak levels, and the system controls solar power to output at full capacity and controls the energy storage batteries to discharge at maximum power, replacing grid power.

[0064] The following will be combined with the appendix Figure 2 This application provides a detailed description of the energy efficiency management device for photovoltaic-storage-charging stations based on dynamic pricing, as provided in the embodiments of this application. It should be noted that the appendix... Figure 2 The energy efficiency management device for photovoltaic-storage-charging stations based on dynamic rates shown is used to perform the functions described in this application. Figure 1 The methods shown in the embodiments are for illustrative purposes only, illustrating the parts relevant to the embodiments of this application. For specific technical details not disclosed, please refer to this application. Figure 1 The example shown.

[0065] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of the photovoltaic-storage-charging station energy efficiency management device based on dynamic rates provided in an embodiment of this application. Figure 2 As shown, the device includes:

[0066] The photovoltaic-storage-charging data preprocessing module 201 is used to acquire historical photovoltaic power, historical charging load and historical weather data, calculate the photovoltaic predicted power and charging load predicted values ​​based on the historical photovoltaic power, historical charging load and historical weather data, and establish power balance constraints based on the photovoltaic predicted power and charging load predicted values.

[0067] The photovoltaic-storage charging efficiency management module 202 is used to acquire photovoltaic-storage charging data, which includes grid data and energy storage data. Based on the grid data and energy storage data, an objective function is established. Based on the objective function and the established constraints, an energy storage charging and discharging plan sequence and a grid interaction plan sequence are obtained. Battery charging and discharging are performed based on the energy storage charging and discharging plan sequence, and grid interaction is performed based on the grid interaction plan sequence.

[0068] In one possible implementation, the photovoltaic energy storage and charging data preprocessing module 201 is specifically used for:

[0069] Energy storage data includes the minimum allowable capacity of the battery, the maximum allowable capacity of the battery, and the current battery capacity. The method further includes:

[0070] A first energy storage constraint is established based on the minimum allowable capacity of the battery, the maximum allowable capacity of the battery, and the current capacity of the battery.

[0071] In one possible implementation, the photovoltaic-storage-charging energy efficiency management module 202 is specifically used for:

[0072] The energy storage data also includes the energy storage battery charge / discharge efficiency and the battery's rated capacity; the method further includes:

[0073] A second energy storage constraint is established based on the energy storage battery's charge and discharge efficiency and the battery's rated capacity. The second energy storage constraint is used to calculate the battery's state of charge at the next moment.

[0074] The state of charge (SOC) of the battery is collected at set time intervals. If the SOC collected by the battery is not equal to the calculated SOC, the energy storage charging and discharging plan sequence and the grid interaction plan sequence are recalculated.

[0075] In one possible implementation, the photovoltaic-storage-charging energy efficiency management module 202 is specifically used for:

[0076] When the state of charge of the collected battery is not equal to the calculated state of charge of the battery, the deviation value between the collected state of charge of the battery and the calculated state of charge of the battery is calculated. If the deviation value exceeds the set deviation threshold for multiple consecutive cycles, the calculation of the energy storage charging and discharging plan sequence and the grid interaction plan sequence is stopped and an alarm message is sent.

[0077] In one possible implementation, the photovoltaic-storage-charging energy efficiency management module 202 is specifically used for:

[0078] If the grid voltage is not within the set grid voltage range, the calculation of the energy storage charging and discharging plan sequence and the grid interaction plan sequence is stopped and an alarm message is sent.

[0079] In one possible implementation, the photovoltaic energy storage and charging data preprocessing module 201 is specifically used for:

[0080] The energy storage data also includes power display of the PCS converter and capacity display of the transformer or grid connection point; the method further includes:

[0081] A first device power constraint condition is established based on the maximum power display of the PCS converter.

[0082] A second equipment power constraint condition is established based on the maximum capacity of the transformer or grid connection point.

[0083] In one possible implementation, the photovoltaic energy storage and charging data preprocessing module 201 is specifically used for:

[0084] After acquiring the photovoltaic-storage-charging data, the data is processed for missing values, outliers, and normalization.

[0085] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0086] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0087] See Figure 3 It shows a schematic diagram of the structure of an electronic device according to an embodiment of this application, which can be used to implement... Figure 1 The method in the illustrated embodiment. (As shown) Figure 3 As shown, the electronic device 300 may include: at least one central processing unit 301, at least one network interface 304, user interface 303, memory 305, and at least one communication bus 302.

[0088] The communication bus 302 is used to enable communication between these components.

[0089] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0090] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0091] The central processing unit 301 may include one or more processing cores. The central processing unit 301 connects to various parts within the electronic device 300 using various interfaces and lines. It executes various functions of the terminal 300 and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the central processing unit 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The central processing unit 301 may integrate one or more of the following: a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the central processing unit 301.

[0092] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned central processing unit 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.

[0093] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and to acquire user input data; while the central processing unit 301 can be used to call the application program stored in the memory 305 and specifically perform the following operations:

[0094] S101. Obtain historical photovoltaic power, historical charging load and historical weather data; calculate the predicted photovoltaic power and predicted charging load based on the historical photovoltaic power, historical charging load and historical weather data; establish power balance constraints based on the predicted photovoltaic power and predicted charging load.

[0095] S102. Acquire photovoltaic-storage charging data, which includes grid data and energy storage data. Establish an objective function based on the grid data and the energy storage data. Obtain an energy storage charging and discharging plan sequence and a grid interaction plan sequence based on the objective function and the established constraints. Perform battery charging and discharging based on the energy storage charging and discharging plan sequence and perform grid interaction based on the grid interaction plan sequence.

[0096] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0097] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0098] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0103] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0104] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for energy efficiency management of photovoltaic-storage-charging stations based on dynamic pricing rates, characterized in that, The method includes: Historical photovoltaic power, historical charging load, and historical weather data are acquired. Based on the historical photovoltaic power, historical charging load, and historical weather data, the predicted photovoltaic power and predicted charging load values ​​are calculated. Power balance constraints are established based on the predicted photovoltaic power and predicted charging load values. Acquire photovoltaic-storage-charging data, which includes grid data and energy storage data. Establish an objective function based on the grid data and energy storage data. Obtain an energy storage charging and discharging plan sequence and a grid interaction plan sequence based on the objective function and the established constraints. Perform battery charging and discharging based on the energy storage charging and discharging plan sequence and perform grid interaction based on the grid interaction plan sequence.

2. The energy efficiency management method for photovoltaic-storage charging stations based on dynamic rates according to claim 1, characterized in that, The energy storage data includes the minimum allowable capacity of the battery, the maximum allowable capacity of the battery, and the current battery capacity. The method further includes: A first energy storage constraint is established based on the minimum allowable capacity of the battery, the maximum allowable capacity of the battery, and the current capacity of the battery.

3. The energy efficiency management method for photovoltaic-storage charging stations based on dynamic rates according to claim 2, characterized in that, The energy storage data also includes the energy storage battery charge / discharge efficiency and the battery's rated capacity; the method further includes: A second energy storage constraint is established based on the energy storage battery's charge and discharge efficiency and the battery's rated capacity. The second energy storage constraint is used to calculate the battery's state of charge at the next moment. The state of charge (SOC) of the battery is collected at set time intervals. If the SOC collected by the battery is not equal to the calculated SOC, the energy storage charging and discharging plan sequence and the grid interaction plan sequence are recalculated.

4. The energy efficiency management method for photovoltaic-storage charging stations based on dynamic rates according to claim 3, characterized in that, The method further includes: When the state of charge of the collected battery is not equal to the calculated state of charge of the battery, the deviation value between the collected state of charge of the battery and the calculated state of charge of the battery is calculated. If the deviation value exceeds the set deviation threshold for multiple consecutive cycles, the calculation of the energy storage charging and discharging plan sequence and the grid interaction plan sequence is stopped and an alarm message is sent.

5. The energy efficiency management method for photovoltaic-storage charging stations based on dynamic rates according to claim 3, characterized in that, The method further includes: If the grid voltage is not within the set grid voltage range, the calculation of the energy storage charging and discharging plan sequence and the grid interaction plan sequence is stopped and an alarm message is sent.

6. The energy efficiency management method for photovoltaic-storage charging stations based on dynamic rates according to claim 1, characterized in that, The energy storage data also includes power display of the PCS converter and capacity display of the transformer or grid connection point; the method further includes: A first device power constraint condition is established based on the maximum power display of the PCS converter. A second equipment power constraint condition is established based on the maximum capacity of the transformer or grid connection point.

7. The energy efficiency management method for photovoltaic-storage charging stations based on dynamic rates according to claim 1, characterized in that, The method further includes: After acquiring the photovoltaic-storage-charging data, the data is processed for missing values, outliers, and normalization.

8. A photovoltaic-storage-charging station energy efficiency management device based on dynamic tariffs, characterized in that, The device includes: The photovoltaic-storage-charging data preprocessing module is used to acquire historical photovoltaic power, historical charging load and historical weather data, calculate the photovoltaic predicted power and charging load predicted values ​​based on the historical photovoltaic power, historical charging load and historical weather data, and establish power balance constraints based on the photovoltaic predicted power and charging load predicted values. The photovoltaic-storage-charging energy efficiency management module is used to acquire photovoltaic-storage-charging data, which includes grid data and energy storage data. An objective function is established based on the grid data and the energy storage data. Based on the objective function and the established constraints, an energy storage charging and discharging plan sequence and a grid interaction plan sequence are obtained. Battery charging and discharging are performed based on the energy storage charging and discharging plan sequence, and grid interaction is performed based on the grid interaction plan sequence.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, the computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the steps of the method as claimed in any one of claims 1-7.

Images